Improved RSSI based data augmentation technique for fingerprint indoor localisation

Rashmi Sharan Sinha, Seung Hoon Hwang

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Recently, deep-learning-based indoor localisation systems have attracted attention owing to their higher performance compared with traditional indoor localization systems. However, to achieve satisfactory performance, the former systems require large amounts of data to train deep learning models. Since obtaining the data is usually a tedious task, this requirement deters the use of deep learning approaches. To address this problem, we propose an improved data augmentation technique based on received signal strength indication (RSSI) values for fingerprint indoor positioning systems. The technique is implemented using available RSSI values at one reference point, and unlike existing techniques, it mimics the constantly varying RSSI signals. With this technique, the proposed method achieves a test accuracy of 95.26% in the laboratory simulation and 94.59% in a real-time environment, and the average location error is as low as 1.45 and 1.60 m, respectively. The method exhibits higher performance compared with an existing augmentation method. In particular, the data augmentation technique can be applied irrespective of the positioning algorithm used.

Original languageEnglish
Article number851
JournalElectronics (Switzerland)
Volume9
Issue number5
DOIs
StatePublished - May 2020

Keywords

  • CNN
  • Fingerprint
  • Indoor positioning
  • RSSI augmentation

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